AutoDebias: Learning to Debias for Recommendation
- URL: http://arxiv.org/abs/2105.04170v1
- Date: Mon, 10 May 2021 08:03:48 GMT
- Title: AutoDebias: Learning to Debias for Recommendation
- Authors: Jiawei Chen, Hande Dong, Yang Qiu, Xiangnan He, Xin Xin, Liang Chen,
Guli Lin, Keping Yang
- Abstract summary: We propose textitAotoDebias that leverages another (small) set of uniform data to optimize the debiasing parameters.
We derive the generalization bound for AutoDebias and prove its ability to acquire the appropriate debiasing strategy.
- Score: 43.84313723394282
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Recommender systems rely on user behavior data like ratings and clicks to
build personalization model. However, the collected data is observational
rather than experimental, causing various biases in the data which
significantly affect the learned model. Most existing work for recommendation
debiasing, such as the inverse propensity scoring and imputation approaches,
focuses on one or two specific biases, lacking the universal capacity that can
account for mixed or even unknown biases in the data.
Towards this research gap, we first analyze the origin of biases from the
perspective of \textit{risk discrepancy} that represents the difference between
the expectation empirical risk and the true risk. Remarkably, we derive a
general learning framework that well summarizes most existing debiasing
strategies by specifying some parameters of the general framework. This
provides a valuable opportunity to develop a universal solution for debiasing,
e.g., by learning the debiasing parameters from data. However, the training
data lacks important signal of how the data is biased and what the unbiased
data looks like. To move this idea forward, we propose \textit{AotoDebias} that
leverages another (small) set of uniform data to optimize the debiasing
parameters by solving the bi-level optimization problem with meta-learning.
Through theoretical analyses, we derive the generalization bound for AutoDebias
and prove its ability to acquire the appropriate debiasing strategy. Extensive
experiments on two real datasets and a simulated dataset demonstrated
effectiveness of AutoDebias. The code is available at
\url{https://github.com/DongHande/AutoDebias}.
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